Troubleshooting DataHub Ingestion and Configuration Issues

ingestion
1

Original Slack Thread

I try to ingest a json schema to our datahub running on Tanzu by using Python Emmiter (“datahub ingest -c recipe.yml”). I tried different IPs, but always get “Unable to connect to XXX with status_code: 403. Please check your configuration and make sure you are talking to the DataHub GMS…”
Which configurations are needed? How to find out the right DataHub host to connect to?
We’ve set DATAHUB_GMS_HOST, DATAHUB_GMS_PORT, GMS_HOST, GMS_PORT, DATAHUB_GMS_URL, DATAHUB_GMS_TOKEN, KAFKA_BOOTSTRAP_SERVER and SCHEMA_REGISTRY_URL

2

Hey there! :wave:

<@U06TM7M40RK> Can you help with the questions in this thread?

:information_source: Please continue to tag RunLLM (e.g <@U06TM7M40RK>) to have it continue generating responses!

3

To address the issue of connecting to DataHub GMS and resolving the “Unable to connect” error, here are some steps and considerations based on the provided data sources:

  1. Verify GMS URL Configuration: Ensure that the DATAHUB_GMS_URL environment variable is correctly set. This variable should point to the URL of your DataHub GMS instance. According to the <https://github.com/datahub-project/datahub/blob/master/docs/cli.md|DataHub CLI documentation>, the DATAHUB_GMS_URL is used to set the URL of the GMS instance. It is recommended to use this variable over individual host and port settings.

  2. Check Authentication Token: If your DataHub instance requires authentication, ensure that the DATAHUB_GMS_TOKEN is set with a valid token. This token is necessary for authenticating requests to the GMS. As per the <https://github.com/datahub-project/datahub/blob/master/docs/cli.md|DataHub CLI documentation>, the DATAHUB_GMS_TOKEN is used for communicating with DataHub Cloud.

  3. Network Configuration: Ensure that there are no network issues preventing the connection to the GMS. This includes checking firewall rules, network policies, and ensuring that the GMS is accessible from the environment where the ingestion is being run.

  4. Correct IP and Port: Double-check the IP and port you are using to connect to the GMS. The error message suggests a 403 status code, which typically indicates a permission issue. Ensure that the IP and port are correct and that the GMS is configured to accept connections from your source.

  5. Debugging: Use the --debug flag when running the ingestion command to get more detailed logs. This can help identify where the connection is failing.

  6. Recipe Configuration: Ensure that your recipe.yml file is correctly configured with the appropriate source and sink settings. The sink should be configured to use the datahub-rest type with the correct server URL and token.

If you need more detailed steps or further assistance, please let me know!

Sources:

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<@U06TM7M40RK> How to ensure that the DATAHUB_GMS_URL environment variable is correctly set?

5

To ensure that the DATAHUB_GMS_URL environment variable is correctly set, you can follow these steps:

  1. Check Current Environment Variable:

    • Open a terminal or command prompt.
    • Run the following command to check the current value of DATAHUB_GMS_URL:
      echo $DATAHUB_GMS_URL
    • This should output the URL you have set for the DataHub GMS instance. Ensure that it is the correct URL.

  2. Set the Environment Variable:

    • If the variable is not set or is incorrect, you can set it using the following command:
      export DATAHUB_GMS_URL="http://your-datahub-gms-url:port"
    • Replace http://your-datahub-gms-url:port with the actual URL and port of your DataHub GMS instance.

  3. Verify the Setting:

    • Run the echo command again to verify that the environment variable is now set correctly:
      echo $DATAHUB_GMS_URL

  4. Persistent Setting:

    • To make the setting persistent across sessions, add the export command to your shell’s configuration file (e.g., .bashrc, .bash_profile, or .zshrc), depending on the shell you are using.

  5. Using .datahubenv File:

    • Alternatively, you can set the DATAHUB_GMS_URL in the .datahubenv file located in your home directory. Add or update the following line in the file:
      DATAHUB_GMS_URL=http://your-datahub-gms-url:port

  6. Debugging:

    • If you continue to experience issues, use the --debug flag with your DataHub CLI commands to get more detailed logs, which can help identify if the correct URL is being used.

These steps should help ensure that the DATAHUB_GMS_URL environment variable is correctly set and recognized by your system.

Would you like more detailed steps or additional information on configuring the DataHub CLI?

Sources:

6

<@U06TM7M40RK> Is the DATAHUB_GMS_URL teh needed DataHub host I set with datahub init? And what is meant by DataHub access token? What should I set there?

7

Yes, the DATAHUB_GMS_URL is the URL of the DataHub Metadata Service (GMS) that you set when you run the datahub init command. This URL tells the DataHub CLI where to connect for metadata operations.

Setting the DataHub Host with datahub init

When you run datahub init, you are prompted to enter the DataHub host, which is essentially the DATAHUB_GMS_URL. This URL should include the protocol (http or https), the hostname or IP address, and the port number of your DataHub GMS instance. For example:

datahub init
Enter your DataHub host [http://localhost:8080]: http://your-datahub-gms-url:port

DataHub Access Token

The DataHub access token is used for authenticating requests to the DataHub GMS. This is particularly important if your DataHub instance requires authentication. The access token is typically a Personal Access Token (PAT) that you generate from your DataHub account settings.

Setting the Access Token

  1. Generate a Personal Access Token:

    • Log into your DataHub instance.
    • Navigate to your account settings and generate a new Personal Access Token.

  2. Set the Access Token:

    • You can set the access token using the DATAHUB_GMS_TOKEN environment variable. For example:
      export DATAHUB_GMS_TOKEN="your-personal-access-token"

  3. Using .datahubenv File:

    • Alternatively, you can set the token in the .datahubenv file in your home directory:
      DATAHUB_GMS_TOKEN=your-personal-access-token

The access token is used to authenticate your CLI commands with the DataHub GMS, ensuring that only authorized users can perform operations.

Would you like more detailed steps or additional information on configuring the DataHub CLI?

Sources:

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<@U06TM7M40RK> Could status_code 403 be caused by missing token?

9

Yes, a status code 403 (Forbidden) can indeed be caused by a missing or incorrect token. In the context of DataHub, if your instance is configured to require authentication, a 403 error typically indicates that the request was not authorized. This can happen if:

  1. Token is Missing: The request to the DataHub GMS does not include the necessary authentication token. The DATAHUB_GMS_TOKEN environment variable should be set with a valid Personal Access Token (PAT) to authenticate the request.

  2. Token is Incorrect or Expired: The token provided is either incorrect or has expired. Ensure that the token is valid and has not been revoked or expired.

  3. Insufficient Permissions: The token does not have the necessary permissions to perform the requested operation. Ensure that the token is generated from an account with the appropriate privileges.

To resolve this, make sure that the DATAHUB_GMS_TOKEN is correctly set with a valid token. You can set it using the following command:

export DATAHUB_GMS_TOKEN="your-personal-access-token"

If you continue to experience issues, verify the token’s validity and permissions, and ensure that it is included in the request headers as expected.

Would you like more detailed steps or additional information on configuring the DataHub CLI?

Sources:

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<@U06TM7M40RK> what is the difference to the DATAHUB_GMS_TOKEN?

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The DATAHUB_GMS_TOKEN is an environment variable used to store a Personal Access Token (PAT) for authenticating requests to the DataHub Metadata Service (GMS). Here’s a breakdown of its purpose and how it differs from other tokens or configurations:

Purpose of DATAHUB_GMS_TOKEN

  1. Authentication: The DATAHUB_GMS_TOKEN is specifically used to authenticate API requests made to the DataHub GMS. It acts as a credential that verifies the identity of the user or service making the request.

  2. Access Control: By using a PAT, DataHub can enforce access control, ensuring that only authorized users or services can perform certain actions, such as ingesting metadata or querying the DataHub.

  3. Security: The token adds a layer of security by requiring that requests include a valid token in the Authorization header. This helps prevent unauthorized access to the DataHub.

Difference from Other Tokens or Configurations

  • Session Tokens: These are typically used for user sessions in the DataHub frontend and are stored in browser cookies. They are different from PATs, which are used for programmatic access.

  • System Client ID and Secret: These are used for system-level authentication within DataHub components and are not the same as PATs. They are configured in the application settings and are used for internal communication between DataHub services.

  • Environment Variables: While DATAHUB_GMS_TOKEN is an environment variable for authentication, other environment variables like DATAHUB_GMS_URL are used to configure the connection details (e.g., host and port) for the GMS.

In summary, the DATAHUB_GMS_TOKEN is specifically for authenticating API requests to the DataHub GMS using a Personal Access Token, whereas other tokens or configurations serve different purposes within the DataHub ecosystem.

Would you like more detailed steps or additional information on configuring the DataHub CLI?

Sources:

12

<@U06TM7M40RK> I’ve updated teh configuration but still get the same status_code 403

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If you’ve updated the configuration and are still encountering a status code 403, here are some additional steps and considerations to help troubleshoot the issue:

  1. Verify Token Validity:

    • Ensure that the Personal Access Token (PAT) you are using is valid and has not expired. You can regenerate a new token from your DataHub account settings if needed.

  2. Check Token Permissions:

    • Confirm that the token has the necessary permissions to perform the requested operation. The account from which the token is generated should have the appropriate roles and access rights.

  3. Correct Authorization Header:

    • Make sure that the token is being correctly included in the Authorization header of your requests. The header should be formatted as follows:
      Authorization: Bearer your-personal-access-token

  4. Network and Firewall Settings:

    • Verify that there are no network or firewall settings blocking access to the DataHub GMS. Ensure that the GMS is accessible from the environment where you are running the ingestion.

  5. Debugging:

    • Use the --debug flag with your DataHub CLI commands to get more detailed logs. This can help identify where the connection is failing or if there are any additional error messages.

  6. Review GMS Logs:

    • Check the logs of the DataHub GMS service for any error messages or warnings that might provide more context on why the request is being denied.

  7. Configuration Consistency:

    • Double-check that all related configurations (e.g., DATAHUB_GMS_URL, DATAHUB_GMS_TOKEN) are consistently set across your environment and match the expected values for your DataHub instance.

If these steps do not resolve the issue, it may be helpful to review the specific error messages in the logs or consult with your network or security team to ensure there are no additional access restrictions in place.

Would you like more detailed steps or additional information on any of these points?

Sources:

14

<@U06TM7M40RK> Can you help me to have the right json schema?

15

To help you with the correct JSON schema for ingestion into DataHub, it’s important to ensure that the schema is compatible with the DataHub metadata model. Here are some general guidelines and an example of how you might structure a JSON schema for a dataset:

General Guidelines for JSON Schema

  1. Schema Type: Ensure that the schema type is supported by DataHub. DataHub primarily supports Avro and Protobuf schemas, but JSON schema support is in an incubating state, which means it might have limitations.

  2. Field Definitions: Define each field with its type, description, and any other relevant metadata. Ensure that the field types are compatible with DataHub’s expected types.

  3. Schema Metadata: Include metadata such as schema name, platform, and any other relevant information that DataHub might require.

Example JSON Schema

Here’s an example of how a JSON schema might be structured for a dataset:

{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "ExampleDataset",
  "type": "object",
  "properties": {
    "field1": {
      "type": "string",
      "description": "Description of field1"
    },
    "field2": {
      "type": "integer",
      "description": "Description of field2"
    },
    "field3": {
      "type": "boolean",
      "description": "Description of field3"
    }
  },
  "required": ["field1", "field2"]
}

Steps to Use JSON Schema with DataHub

  1. Validate the Schema: Use a JSON schema validator to ensure that your schema is correctly formatted and adheres to the JSON schema standards.

  2. Ingest the Schema: Use the DataHub CLI or API to ingest the schema. Ensure that your ingestion configuration is set up to handle JSON schemas.

  3. Check Compatibility: Since JSON schema support is incubating, check the DataHub documentation or community forums for any specific configurations or limitations related to JSON schema ingestion.

If you have a specific schema or dataset in mind, please provide more details, and I can offer more tailored guidance.

Would you like more detailed steps or additional information on configuring the ingestion process?

Sources:

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<@U06TM7M40RK> I need to upload important metadata from a rdf schema. How can I do that? What preperation steps are needed?

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Hi :slight_smile: ! Did this interaction resolve your issue or do you still require assistance ?

Please tag @ RunLLM if you still need further assistance !

Or tag @Datahub Community Support if you require a person to take a look :slight_smile:

Hope this helps ! ><

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<@U07FV3UHE4D> How can I upload an rdf schema? Is it possible in jsonld? What preparation steps are needed?
I tried a recipe with source “file” and my jsonld, but I get “No metadata was produced by the source”

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Thank you for letting us know you need a person to take a look at this thread.

Either a community member or one of our team members will try to assist when they have a moment, responses unfortunately might not be immediate. We hope this helps speed things along :slight_smile: